Course

Evals & Reliability

Anyone can call the same model API you do. What they can't copy is knowing whether your system actually works — and noticing the day a prompt tweak silently makes it worse. This is the engineer's answer to 'looks good to me': build a test set, grade open-ended output with an LLM judge, wire evals into CI, and trace real requests in production. Six short, practical lessons, every screen drawn, no framework lock-in.

6 lessonsIntermediate42 min

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Course lessons

Why evals are the real moat

The model is a commodity. Knowing it works — and catching regressions — is the moat.

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Building a test set

Collect real inputs, write down the expected behavior, and run them as pass/fail cases.

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LLM-as-judge

When there's no exact right answer, have a model grade the output against a rubric.

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Regression & CI for prompts

Treat prompts like code: every change runs the eval suite, and a score drop blocks the merge.

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Observability & tracing

You can't fix what you can't see. Log and trace every step of a request in production.

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Shipping with confidence

A quick recap — and the one discipline that ties reliable AI systems together.

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Learn it properly — tracked & personalized

Follow the whole course as a guided journey — sequential lessons, progress, streaks and a personalized path, all in the app.

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